Applying Deep Embedded-Self-Organizing Map (DE-SOM) Method to Separate Geochemical Anomalous Areas of Copper-Gold Mineralization in Moalleman Region, Iran | ||
Journal of Mining and Environment | ||
مقاله 16، دوره 16، شماره 5، مهر و آبان 2025، صفحه 1781-1794 اصل مقاله (3.87 M) | ||
نوع مقاله: Original Research Paper | ||
شناسه دیجیتال (DOI): 10.22044/jme.2024.15003.2861 | ||
نویسندگان | ||
Zohre Hoseinzade* ؛ Mohammad Hassan Bazoobandi | ||
Department of Science Education, Farhangian University, P.O. Box 14665-889, Tehran, Iran | ||
چکیده | ||
Anomaly detection is the process of recognizing patterns in data that differ from the typical behavior. In geochemistry, this involves identifying hidden patterns and unusual components within the context of exploratory target identification. This issue is particularly significant when limited information is available about the area of interest. Therefore, employing methods that can aid in the exploration process under such conditions and with limited data is highly valuable. In this study, the Deep-Embedded Self-Organizing Map (DE-SOM), an unsupervised deep learning approach, was used to detect geochemical anomalies. The research focused on identifying multivariate geochemical anomalies in the Moalleman region. After detecting the region's geochemical anomalies, the effectiveness of the algorithm was assessed alongside two other types of SOM algorithms. For this purpose, the prediction area plot was utilized, with the intersection points for DE-SOM, Batch SOM, and SOM were determined to be 0.75, 0.67, and 0.65, respectively. The multivariate geochemical anomaly in the Moalleman area shows a good correlation with known mineral occurrences and the andesite and dacite units. Based on this, it can be stated that the DE-SOM method is a useful tool for identifying anomalies and patterns associated with mineralization. | ||
کلیدواژهها | ||
Geochemical Anomaly؛ Deep Embedded SOM ، Batch SOM، SOM؛ Moalleman | ||
مراجع | ||
[1] Zekri, H., Mokhtari, A.R. and Cohen, D.R. (2019). Geochemical pattern recognition through matrix decomposition. Ore Geology Reviews, 104, 670-685. https://doi.org/10.1016/j.oregeorev.2018.11.026
[2] Shahrestani, S. and Mokhtari, A.R. (2017). Improved detection of anomalous catchment basins by incorporating drainage density in dilution correction of geochemical residuals. Geochemistry: Exploration, Environment, Analysis, 17(3), 194-203. https://doi.org/10.1144/geochem2016-015
[3] Hoseinzade, Z., Mokhtari, A.R. and Zekri, H. (2018). Application of radial basis function in the analysis of irregular geochemical patterns through spectrum-area method. Journal of Geochemical Exploration, 194, 257-265. https://doi.org/10.1016/j.gexplo.2018.09.002
[4] Shahrestani, S. and Mokhtari, A.R. (2017). Dilution correction equation revisited: The impact of stream slope, relief ratio and area size of basin on geochemical anomalies. Journal of African Earth Sciences, 128, 16-26. https://doi.org/10.1016/j.jafrearsci.2016.06.019
[5] Hosseini, S.A., Khah, N.K.F., Kianoush, P., Afzal, P., Ebrahimabadi, A. and Shirinabadi, R. (2023). Integration of fractal modeling and correspondence analysis reconnaissance for geochemically high-potential promising areas, NE Iran. Results in Geochemistry, 11, 100026. https://doi.org/10.1016/j.ringeo.2023.100026
[6] Afzal, P., Khakzad, A., Moarefvand, P., Omran, N.R., Esfandiari, B. and Alghalandis, Y.F. (2010). Geochemical anomaly separation by multifractal modeling in Kahang (Gor Gor) porphyry system, Central Iran. Journal of Geochemical Exploration, 104(1-2), 34-46. https://doi.org/10.1016/j.gexplo.2009.11.003
[7] Yousefi, M. (2017). Analysis of Zoning Pattern of Geochemical Indicators for Targeting of Porphyry-Cu Mineralization: A Pixel-Based Mapping Approach. Natural Resources Research, 26, 429-441.https://doi.org/10.1007/s11053-017-9334-7
[8] Yousefi, M. (2017). Recognition of an enhanced multi-element geochemical signature of porphyry copper deposits for vectoring into mineralized zones and delimiting exploration targets in Jiroft area, SE Iran. Ore Geology Reviews, 83, 200-214. https://doi.org/10.1016/j.oregeorev.2016.12.024
[9] Mahdiyanfar, H. and Seyedrahimi-Niaraq, M. (2022). Improvement of geochemical prospectivity mapping using power spectrum–area fractal modelling of the multi-element mineralization factor (SAF-MF). Geochemistry: Exploration, Environment, Analysis, 22(4), geochem2022-015. https://doi.org/10.1144/geochem2022-015
[10] Seyedrahimi-Niaraq, M., Mahdiyanfar, H. and Mokhtari, A.R. (2023). Application of geochemical structural methods to determine lead-contaminated areas related to mining activities. Journal of Analytical and Numerical Methods in Mining Engineering, 13(34), 41-55. https://doi.org/10.22034/ANM.2022.2783
[11] Mahdiyanfar, H. and Niaraq, M.S. (2023). Integration of Fractal and Multivariate Principal Component Models for Separating Pb-Zn Mineral Contaminated Areas. Journal of Mining and Environment, 14(3), 1019-1035. https://doi.org/10.22044/jme.2023.13227.2424
[12] Hoseinzade, Z. and Mokhtari, A.R. (2017). A comparison study on detection of key geochemical variables and factors through three different types of factor analysis. Journal of African Earth Sciences, 134, 557-563. https://doi.org/10.1016/j.jafrearsci.2017.07.025
[13] Zuo, R., Xiong, Y., Wang, J. and Carranza, E.J.M. (2019). Deep learning and its application in geochemical mapping. Earth Sci Rev, 192, 1-14. https://doi.org/10.1016/j.earscirev.2019.02.023
[14] Farhadi, S., Tatullo, S., Boveiri Konari, M. and Afzal, P. (2024). Evaluating StackingC and ensemble models for enhanced lithological classification in geological mapping. Journal of Geochemical Exploration, 260, 107441. https://doi.org/10.1016/j.gexplo.2024.107441
[15] Pourgholam, M.M., Afzal, P., Adib, A., Rahbar, K. and Gholinejad, M. (2024). Recognition of REEs anomalies using an image Fusion fractal-wavelet model in Tarom metallogenic zone, NW Iran. Geochemistry, 84(2), 126093. https://doi.org/10.1016/j.chemer.2024.126093
[16] Afzal, P., Farhadi, S., Konari, M.B., Meigoony, M.S. and Saein, L.D. (2022). Geochemical anomaly detection in the Irankuh District using Hybrid Machine learning technique and fractal modeling. Geopersia, 12(1), 191-199. https://doi.org/10.22059/GEOPE.2022.336072.648644
[17] Soltani, Z., Hassani, H. and Esmaeiloghli, S. (2024). A deep autoencoder network connected to geographical random forest for spatially aware geochemical anomaly detection. Computers & Geosciences, Pergamon. 190, 105657. https://doi.org/10.1016/J.CAGEO.2024.105657
[18] Saremi, M., Bagheri, M., Agha Seyyed Mirzabozorg, S.A., Hassan, N.E., Hoseinzade, Z., Maghsoudi, A. et al. (2024). Evaluation of Deep Isolation Forest (DIF) Algorithm for Mineral Prospectivity Mapping of Polymetallic Deposits. Minerals, Vol 14, Page 1015, Multidisciplinary Digital Publishing Institute. 14, 1015. https://doi.org/10.3390/MIN14101015
[19] Esmaeiloghli, S., Tabatabaei, S.H. and Carranza, E.J.M. (2023). Empirical mode decomposition and power spectrum filtering for detection of frequency channels related to multi-scale geochemical anomalies: Metal exploration targeting in Moalleman district, NE Iran. Journal of Geochemical Exploration, Elsevier. 246, 107157. https://doi.org/10.1016/J.GEXPLO.2023.107157
[20] Esmaeiloghli, S., Tabatabaei, S.H. and Carranza, E.J.M. (2023). Infomax-based deep autoencoder network for recognition of multi-element geochemical anomalies linked to mineralization. Computers & Geosciences, Pergamon. 175, 105341. https://doi.org/10.1016/J.CAGEO.2023.105341
[21] Abedi, M., Norouzi, G.H. and Torabi, S.A. (2013). Clustering of mineral prospectivity area as an unsupervised classification approach to explore copper deposit. Arabian Journal of Geosciences, 6, 3601-3613. https://doi.org/10.1007/s12517-012-0615-5
[22] Kohonen, T. (2012). Self-organization and associative memory (Vol. 8). Springer Science & Business Media.
[23] Kohonen, T. (1997, June). Exploration of very large databases by self-organizing maps. In Proceedings of international conference on neural networks (icnn'97) (Vol. 1, pp. PL1-PL6). IEEE.
[24] Kohonen, T., (1990). The self-organizing map. Proc IEEE 78, 1464–1480,
[25] Sarparandeh, M. and Hezarkhani, A. (2016). Application of Self-Organizing Map for Exploration of REEs’ Deposition. Open Journal of Geology, 6(07), 571. https://doi.org/10.4236/ojg.2016.67045
[26] Žibret, G. and Šajn, R. (2010). Hunting for geochemical associations of elements: Factor analysis and self-organising maps. Mathematical Geosciences, 42, 681-703. https://doi.org/10.1007/s11004-010-9288-3
[27] Yu, X., Xiao, F., Zhou, Y., Wang, Y. and Wang, K. (2019). Application of hierarchical clustering, singularity mapping, and Kohonen neural network to identify Ag-Au-Pb-Zn polymetallic mineralization associated geochemical anomaly in Pangxidong district. Journal of Geochemical Exploration, Elsevier. 203, 87–95. https://doi.org/10.1016/J.GEXPLO.2019.04.007
[28] Mirzabozorg, S.A.A.S., Abedi, M. and Ahmadi, F. (2023). Clustering of Areas Prone to Iron Mineralization in Esfordi Range based on a Hybrid Method of Knowledge- and Data-Driven Approaches. Journal of Mineral Resources Engineering, 8(4): 1-26,
[29] Modabberi, S., Ahmadi, A. and Tangestani, M.H. (2017). Sub-pixel mapping of alunite and jarosite using ASTER data; a case study from north of Semnan, north central Iran. Ore Geology Reviews, 80, 429-436. https://doi.org/10.1016/j.oregeorev.2016.07.014
[30] Yousefi, F., Sadeghian, M., Wanhainen, C., Ghasemi, H. and Frei, D. (2017). Geochemistry, petrogenesis and tectonic setting of middle Eocene hypabyssal rocks of the Torud–Ahmad Abad magmatic belt: An implication for evolution of the northern branch of Neo-Tethys Ocean in Iran. Journal of Geochemical Exploration, 178, 1-15. https://doi.org/10.1016/j.gexplo.2017.03.008
[31] Haghipour, A.S.A., (1985). Geological Map of Iran.
[32] Torshizian, H., Afzal, P., Rahbar, K., Yasrebi, A.B., Wetherelt, A. and Fyzollahhi, N. (2021). Application of modified wavelet and fractal modeling for detection of geochemical anomaly. Geochemistry, 81. https://doi.org/10.1016/j.chemer.2021.125800
[33] Esmaeiloghli, S., Lima, A. and Sadeghi, B. (2024). Lithium exploration targeting through robust variable selection and deep anomaly detection: An integrated application of sparse principal component analysis and stacked autoencoders. Geochemistry, Urban & Fischer. 126111. https://doi.org/10.1016/J.CHEMER.2024.126111
[34] Mohamadi,. Z,., Ziaii, M. and Ziaii. M. (2018). Application of PCA method for hydrothermal alteration mapping in the Torud (Shahrood) area based on ASTER and Sentinel-2A multispectral data. The 36th National and the 3rd International Geosciences Congress of Iran, Tehran, Iran,
[35] Forest, F., Lebbah, M., Azzag, H. and Lacaille, J. (2021). Deep embedded self-organizing maps for joint representation learning and topology-preserving clustering. Neural Computing and Applications, 33. https://doi.org/10.1007/s00521-021-06331-w
[36] Yousefi, M., M.M., G.S.E., G.S., & D.S.L. (2019). Delineation of podiform-type chromite mineralization using geochemical mineralization prospectivity index and staged factor analysis in Balvard area (SE Iran). J Min Environ, 10, 705–15.
[37] Afzal, P., Mirzaei, M., Yousefi, M., Adib, A., Khalajmasoumi, M., Zarifi, A.Z. et al. (2016). Delineation of geochemical anomalies based on stream sediment data utilizing fractal modeling and staged factor analysis. Journal of African Earth Sciences, 119, 139-149. https://doi.org/10.1016/j.jafrearsci.2016.03.009
[38] Yousefi, M., Kamkar-Rouhani, A. and Carranza, E.J.M. (2014). Application of staged factor analysis and logistic function to create a fuzzy stream sediment geochemical evidence layer for mineral prospectivity mapping. Geochemistry: Exploration, Environment, Analysis, 14(1), 45-58. https://doi.org/10.1144/geochem2012-144
[39] Saremi, M., Maghsoudi, A., Ghezelbash, R., Yousefi, M. and Hezarkhani, A. (2024). Targeting of porphyry copper mineralization using a continuous-based logistic function approach in the Varzaghan district, north of Urumieh-Dokhtar magmatic arc. Journal of Mining and Environment,
[40] Saremi, M., Yousefi, S. and Yousefi, M. (2023). Separation of geochemical anomalies related to hydrothermal copper mineralization using staged factor analysis in Feizabad geological map. Journal of Analytical and Numerical Methods in Mining Engineering, 14 (38), 35–44.
[41] Saremi, M., Hoseinzade, Z., Mirzabozorg, S.A.A.S., Pour, A.B., Zoheir, B. and Almasi, A. (2024). Integrated remote sensing and geochemical studies for enhanced prospectivity mapping of porphyry copper deposits: A case study from the Pariz district, Urmia-Dokhtar metallogenic belt, southern Iran. Remote Sensing Applications: Society and Environment, Elsevier. 36, 101343. https://doi.org/10.1016/J.RSASE.2024.101343
[42] Yousefi, M. and Carranza, E.J.M. (2015). Prediction-area (P-A) plot and C-A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling. Computers and Geosciences, 79, 69-81. https://doi.org/10.1016/j.cageo.2015.03.007
[43] Mokhtari, M., Hoseinzade, Z. and Shirani, K. (2020). A comparison study on landslide prediction through FAHP and Dempster–Shafer methods and their evaluation by P–A plots. Environmental Earth Sciences, 79, 1-13. https://doi.org/10.1007/s12665-019-8804-0
[44] Hoseinzade, Z., Zavarei, A. and Shirani, K. (2021). Application of prediction–area plot in the assessment of MCDM methods through VIKOR, PROMETHEE II, and permutation. Natural Hazards, 109, 2489-2507. https://doi.org/10.1007/s11069-021-04929-w
[45] Saremi, M., Yousefi, S. and Yousefi, M. (2024). Combination of geochemical and structural data to determine the exploration target of copper hydrothermal deposits in the Feizabad district. Journal of Mining and Environment, 15(3), 1089-1101.
[46] Saremi, M., Maghsoudi, A., Hoseinzade, Z. and Mokhtari, A.R. (2024). Data-driven AHP: a novel method for porphyry copper prospectivity mapping in the Varzaghan District, NW Iran. Earth Science Informatics, Springer Science and Business Media Deutschland GmbH. 1–16. https://doi.org/10.1007/S12145-024-01481-6/METRICS
[47] Huang, D., Zuo, R. and Wang, J. (2022). Geochemical anomaly identification and uncertainty quantification using a Bayesian convolutional neural network model. Applied Geochemistry, 146, 105450. https://doi.org/10.1016/j.apgeochem.2022.105450
[48] Wang, J. and Zuo, R. (2022). Model averaging for identification of geochemical anomalies linked to mineralization. Ore Geology Reviews, 146, 104955. https://doi.org/10.1016/j.oregeorev.2022.104955
[49] Yousefi, M., Lindsay, M.D. and Kreuzer, O. (2024). Mitigating uncertainties in mineral exploration targeting: Majority voting and confidence index approaches in the context of an exploration information system (EIS). Ore Geology Reviews, 105930. https://doi.org/10.1016/j.oregeorev.2024.105930
[50]. Ihara, C. K. (1981). Maximin and other decision principles. Philosophical topics, 12(3), 59-72.
| ||
آمار تعداد مشاهده مقاله: 306 تعداد دریافت فایل اصل مقاله: 91 |